Contents

DAAO venues and organisations

DAAO venues and organisations

This is an exploratory data analysis of collected data from DAAO. We focus on venues and organisations adopting natural language processing to reveal patterns in the data.

The visualisations consist of…

  • word clouds

  • dendrograms

  • time series and temporal bar charts

import numpy as np
from collections import Counter
import plotly.express as px
import plotly.graph_objects as go
import requests, gzip, io, json, pandas as pd

def fetch_data(ftype='csv', acdedata='event'):
    urls = pd.read_csv('data/data_urls.csv')
    sharelink = urls[urls.data == acdedata][ftype].values[0]
    url = f'https://drive.google.com/u/0/uc?id={sharelink}&export=download&confirm=yes'
    response = requests.get(url)
    decompressed_data = gzip.decompress(response.content)
    decompressed_buffer = io.StringIO(decompressed_data.decode('utf-8'))
    try:
        if ftype == 'csv': df = pd.read_csv(decompressed_buffer, low_memory=False)
        else: df = [json.loads(jl) for jl in pd.read_json(decompressed_buffer, lines=True, orient='records')[0]]
        return pd.DataFrame(df)
    except: return None

    # with requests.get(url, stream=True) as response:
    #     response.raise_for_status()
    #     with gzip.GzipFile(fileobj=response.raw) as f:
    #         if filetype == 'csv': df = pd.read_csv(f, low_memory=False)
    #         else: df = pd.json_normalize([json.loads(json.loads(line)) for line in f])

# # read in data
# dfs = fetch_data(ftype='csv', acdedata='event') #42 secs

# dfs_expanded = []

# # get start and end years - 20 secs
# for idx,row in dfs[dfs.data_source == 'DAAO'].iterrows():
#     try: 
#         this_locations = pd.json_normalize(eval(row['locations'].replace("ObjectId('","'").replace("')","'")))

#         for idx2, row2 in this_locations.iterrows():
#             try: start_yr = row2['coverage_range.date_range.date_start.year']
#             except: start_yr = None

#             try: end_yr = row2['coverage_range.date_range.date_end.year']
#             except: end_yr = None

#             try: place_address = row2['coverage_range.place.ori_address']
#             except: place_address = None

#             try: 
#                 latitude = row2['coverage_range.place.geo_coord.latitude']
#                 longitude = row2['coverage_range.place.geo_coord.longitude']
#             except:
#                 latitude = None; longitude = None

#             row['start_year'] = int(start_yr); row['end_year'] = int(end_yr)
#             row['latitude'] = latitude; row['longitude'] = longitude
#             row['place_address'] = place_address
#             dfs_expanded.append(row)

#     except:
#         start_yr = None; end_yr = None; latitude = None; longitude = None; place_address = None
#         dfs_expanded.append(row)

# dfs_expanded = pd.DataFrame(dfs_expanded)

# # pd.DataFrame(dfs_expanded).to_csv('data/acde_event_expanded.csv', index=False)

# read in expanded data
dfs_expanded = pd.read_csv('data/acde_event_expanded.csv', low_memory=False)

locations_cond = dfs_expanded.latitude.notnull()
year_cond = dfs_expanded.start_year.notnull()
desc_cond = dfs_expanded.description.notnull()
period_cond = (dfs_expanded.start_year >= 1900) & (dfs_expanded.start_year <= 2020)

print('In DAAO,') 
print(f'- there are {dfs_expanded.shape[0]} events.')
print(f'- there are {dfs_expanded[year_cond].shape[0]} events with dates.')
print(f'- there are {dfs_expanded[locations_cond & year_cond].shape[0]} events with geocodes and dates.')
print(f'- there are {dfs_expanded[locations_cond & year_cond & desc_cond].shape[0]} events with geocodes, dates and biographical information.')
print(f'- there are {dfs_expanded[locations_cond & year_cond & desc_cond & period_cond].shape[0]} events with geocodes, dates and biographical information after filtering for events between 1900 and 2020.')

In DAAO,
- there are 22524 events.
- there are 21242 events with dates.
- there are 17764 events with geocodes and dates.
- there are 7751 events with geocodes, dates and biographical information.
- there are 7581 events with geocodes, dates and biographical information after filtering for events between 1900 and 2020.

dfs_rich = dfs_expanded[locations_cond & year_cond & desc_cond & period_cond].copy()

type_count = dfs_rich['types'].value_counts().reset_index().rename(columns={'index':'type','types':'count'})
type_count['prop'] = round((type_count['count']/dfs_rich.shape[0])*100,2)
exh_prop = type_count['type'].str.contains('exhibition')
other_prop = type_count['type'].str.contains('other event')
fest_prop = type_count['type'].str.contains('festival')
perf_prop = type_count['type'].str.contains('performance')

# # create a decade column
dfs_rich['decade_start'] = [str(int(x))[:3]+'0' for x in dfs_rich['start_year']]
dfs_rich['decade_start'] = dfs_rich['decade_start'].astype(int)

print('\nWe use the 7,581 events for the remainder of the analysis. In this subset,') 
print(f'- there are {type_count[exh_prop]["count"].sum()} exhibitions ({round(type_count[exh_prop]["prop"].sum(),2)}%).')
print(f'- there are {type_count[other_prop]["count"].sum()} other-events ({round(type_count[other_prop]["prop"].sum(),2)}%).')
print(f'- there are {type_count[fest_prop]["count"].sum()} festivals ({round(type_count[fest_prop]["prop"].sum(),2)}%).')
print(f'- there are {type_count[perf_prop]["count"].sum()} performance-event ({round(type_count[perf_prop]["prop"].sum(),2)}%).')
print(f'- there are {dfs_rich["types"].isnull().sum()} events with missing type data ({round((dfs_rich["types"].isnull().sum()/dfs_rich.shape[0])*100,2)}%).')

print('\nIn terms of time,')
for t in range(1900,2021,10):
    if t != 2020: print(f'- there are {dfs_rich[dfs_rich.decade_start == t].shape[0]} events between {t}-{t+10} ({round((dfs_rich[dfs_rich.decade_start == t].shape[0]/dfs_rich.shape[0])*100,2)}%).')
    else: print(f'- there are {dfs_rich[dfs_rich.start_year == t].shape[0]} events in {t} ({round((dfs_rich[dfs_rich.start_year == t].shape[0]/dfs_rich.shape[0])*100,2)}%).')

We use the 7,581 events for the remainder of the analysis. In this subset,
- there are 6940 exhibitions (91.55%).
- there are 86 other-events (1.13%).
- there are 32 festivals (0.42%).
- there are 21 performance-event (0.28%).
- there are 508 events with missing type data (6.7%).

In terms of time,
- there are 33 events between 1900-1910 (0.44%).
- there are 31 events between 1910-1920 (0.41%).
- there are 39 events between 1920-1930 (0.51%).
- there are 40 events between 1930-1940 (0.53%).
- there are 41 events between 1940-1950 (0.54%).
- there are 90 events between 1950-1960 (1.19%).
- there are 159 events between 1960-1970 (2.1%).
- there are 651 events between 1970-1980 (8.59%).
- there are 1413 events between 1980-1990 (18.64%).
- there are 2272 events between 1990-2000 (29.97%).
- there are 2283 events between 2000-2010 (30.11%).
- there are 526 events between 2010-2020 (6.94%).
- there are 3 events in 2020 (0.04%).

Most frequent geocodes

Before jumping into the visuals, we inspect the nuances of the geocodes attached to exhibtion data. We find that the geocodes are not always accurate, and that there are many distinct venues representing the same geocode. Further pre-processing will need to be conducted to ensure downstream trends can be accurately identified.

Below is a list of the top 20 geocodes, and the number of events they represent. Through further inspection we can see which geocodes accurate represent the place names.

most_freq_geocodes = dfs_rich[['latitude','longitude']].value_counts().reset_index().rename(columns={0:'Frequency'})
most_freq_geocodes = most_freq_geocodes.head(20)

# get the most frequent place_address for each geocode
for idx,row in most_freq_geocodes.iterrows():
    place_address = dfs_rich[(dfs_rich.latitude == row['latitude']) & (dfs_rich.longitude == row['longitude'])]['place_address'].value_counts().index[0]

    try: place_address2 = dfs_rich[(dfs_rich.latitude == row['latitude']) & (dfs_rich.longitude == row['longitude'])]['place_address'].value_counts().index[1]
    except: place_address2 = None
    
    try: place_address3 = dfs_rich[(dfs_rich.latitude == row['latitude']) & (dfs_rich.longitude == row['longitude'])]['place_address'].value_counts().index[2]
    except: place_address3 = None

    try: place_address4 = dfs_rich[(dfs_rich.latitude == row['latitude']) & (dfs_rich.longitude == row['longitude'])]['place_address'].value_counts().index[3]
    except: place_address4 = None

    try: place_address5 = dfs_rich[(dfs_rich.latitude == row['latitude']) & (dfs_rich.longitude == row['longitude'])]['place_address'].value_counts().index[4]
    except: place_address5 = None
    
    most_freq_geocodes.loc[idx,'most_frequent_address'] = place_address
    most_freq_geocodes.loc[idx,'2ndmost_frequent_address'] = place_address2
    most_freq_geocodes.loc[idx,'3rdmost_frequent_address'] = place_address3
    most_freq_geocodes.loc[idx,'4thmost_frequent_address'] = place_address4
    most_freq_geocodes.loc[idx,'5thmost_frequent_address'] = place_address5

most_freq_geocodes
latitude longitude Frequency most_frequent_address 2ndmost_frequent_address 3rdmost_frequent_address 4thmost_frequent_address 5thmost_frequent_address
0 -34.928621 138.599959 936 Experimental Art Foundation, Adelaide, SA Contemporary Art Centre of South Australia, Ad... Adelaide Central Gallery, Adelaide, SA Greenaway Art Gallery, Adelaide, SA Flinders University Art Museum, Adelaide, SA
1 -33.868901 151.207091 898 Artspace, Sydney, NSW Sydney, NSW Sherman Galleries, Sydney, NSW Australian Centre for Photography, Sydney, NSW Ivan Dougherty Gallery, Sydney, NSW
2 -37.813187 144.962980 502 Monash University Museum of Art, Melbourne, VIC Australian Centre for Contemporary Art, Melbou... RMIT Gallery, Melbourne, VIC Karen Woodbury Gallery, Melbourne, Vic. Melbourne, Vic.
3 -27.470933 153.023502 494 Institute of Modern Art, Brisbane, QLD Brisbane City Art Gallery, Brisbane, Qld Brisbane, Qld University of Queensland Art Museum, Brisbane,... Brisbane, QLD
4 -33.868627 151.217062 321 Art Gallery of New South Wales, Sydney, NSW Art Gallery of NSW, Sydney, NSW Art Gallery Of New South Wales, Sydney, NSW [National] Art Gallery of New South Wales, Syd... Art Gallery of New South Wales, Sydney, 14 Oct...
5 -34.416667 150.883333 192 Wollongong City Gallery, Wollongong, NSW Wollongong City Art Gallery, Wollongong, NSW Project Contemporary Artspace, Wollongong, NSW Wollongong City Gallery, NSW Project Centre for Contemporary Art, Wollongon...
6 -12.437368 130.833821 152 Museums and Art Galleries of the Northern Terr... Museum and Art Gallery of the Northern Territo... Museum & Art Gallery of the Northern Territory... Museum and Art Gallery of the Northern Territo... Museums and Art Galleries of the Northern Terr...
7 -37.822536 144.969113 134 National Gallery of Victoria, Melbourne, VIC National Gallery of Victoria, Melbourne, Vic. Ian Potter Centre, National Gallery of Victori... Access Gallery, National Gallery of Victoria, ... National GAllery of Victoria, Melbourne, Vic.
8 -27.472096 153.018173 133 Queensland Art Gallery, Brisbane, QLD Queensland Art Gallery, Brisbane, Qld Queensland Art Gallery, Brisbane, Qld. Queensland Art Gallery, Brisbane. QLD Queensland Art Gallery, Brsbane, QLD
9 -32.929205 151.772973 126 Newcastle Region Art Gallery, Newcastle, NSW Newcastle City Art Gallery, Newcastle, New Sou... Newcastle Regional Art Gallery, Newcastle, NSW Newcastle Regional Art Gallery, NSW None
10 -31.952222 115.858889 117 John Curtin Gallery, Curtin University, Perth, WA Perth, WA Perth Institute of Contemporary Art, Perth, WA Greenhill Galleries, Perth, WA Katanning Arts Centre, Katanning & Western Aus...
11 -33.281886 149.102659 109 Orange Regional Gallery, Orange, NSW Orange Regional Art Gallery, Orange, NSW Orange Regional Gallery, NSW Orange Regional Gallery None
12 -38.385530 142.482454 108 Warrnambool Art Gallery, Warrnambool, VIC Warrnambool Art Gallery, VIC None None None
13 -31.950414 115.860604 106 Art Gallery of Western Australia, Perth, WA Art Gallery of Western Australia, Perth, WA (a... None None None
14 -33.872355 151.224854 103 8 Llankelly Place, Kings Cross, Sydney, 2011 None None None None
15 -41.437456 147.134155 95 Queen Victoria Museum and Art Gallery, Launces... Queen Victoria Museum and Art Gallery, Launces... Queen Victoria Museum and Art Gallery, Launces... None None
16 -33.884611 151.226586 94 Ivan Dougherty Gallery, COFA, UNSW, Paddington... Roslyn Oxley9 Gallery, Paddington, Sydney, NSW Mary Place Gallery, Paddington, NSW Kaliman Gallery, Paddington, Sydney, NSW Global Gallery, Paddington, NSW
17 -37.562107 143.856149 92 Ballarat Fine Art Gallery, Ballarat, VIC Ballarat, VIC Ballarat Fine Art Gallery, Ballarat, Vic. None None
18 -33.888493 151.187100 86 Tin Sheds Gallery, University of Sydney, Sydne... Sydney College of the Arts, University of Sydn... Tin Sheds, University of Sydney, New South Wales Tinsheds Gallery, University of Sydney, Sydney... SCA Galleries, Sydney College of the Arts, Uni...
19 -37.758900 145.084301 85 Heide Museum of Modern Art, Melbourne, VIC Heide Museum of Modern Art, Bulleen, Vic. Heide Museum of Modern Art, VIC Heide Museum of Modern Art, Bulleen, VIC None

Top 10 geocoded places with most events

After omitting geocodes with random or erroneous place names, we can see that the “true” top 10 geocoded places with the most events. As expected these are all art galleries - all with over 100 events.

Venue

City

Frequency

Art Gallery of New South Wales

Sydney, NSW

321

Wollongong City Gallery

Wollongong, NSW

192

Museums and Art Galleries of the Northern Territory

Darwin, NT

152

National Gallery of Victoria

Melbourne, VIC

134

Queensland Art Gallery

Brisbane, QLD

133

Newcastle Region Art Gallery

Newcastle, NSW

126

Orange Regional Gallery

Orange, NSW

109

Warrnambool Art Gallery

Warrnambool, VIC

108

Art Gallery of Western Australia

Perth, WA

106

8 Llankelly Place, Kings Cross

Sydney, NSW

103

Data pre-processing

We split the cleaning process into two parts.

  • First, we use the geopy library to convert the place names into geocodes. This helps us identify erroneous place names, and also helps us identify duplicate venues. There are only 34.2% of venues that have a geocode attached to them - these are handled accordingly keeping only venues with more than 4 exhibitions.

  • The remaning venues are cleaned using edit distancing. If two place names share more than 75% common characters, then they are considered the same place. This is a very conservative threshold, but it is necessary to ensure that we do not merge two distinct places. Again we only keep venues with more than 4 exhibitions.

Cleaning by aggregating by geocode

# import geopandas # !pip install geopandas
# import geopy # !pip install geopy
# from geopy.geocoders import Nominatim
# from geopy.extra.rate_limiter import RateLimiter

# # 40 min process
# locator = Nominatim(user_agent="myGeocoder")

# # 1 - conveneint function to delay between geocoding calls
# geocode = RateLimiter(locator.geocode, min_delay_seconds=1)

# # 2- - create location column
# venue_addresses['location'] = venue_addresses['address_prompt'].apply(geocode)

# # 3 - create longitude, laatitude and altitude from location column (returns tuple)
# venue_addresses['point'] = venue_addresses['location'].apply(lambda loc: tuple(loc.point) if loc else None)

# # 4 - split point column into latitude, longitude and altitude columns
# venue_addresses[['latitude2', 'longitude2', 'altitude2']] = pd.DataFrame(venue_addresses['point'].tolist(), index=venue_addresses.index)

# # save csv
# venue_addresses.to_csv('venue_addresses_geocoded.csv',index=False)

# read csv
venue_addresses = pd.read_csv('data/venue_addresses_geocoded.csv')

print(f'{round(venue_addresses["point"].notnull().sum()/venue_addresses.shape[0],4)*100}% of the addresses could be geocoded.')

34.2% of the addresses could be geocoded.

# join the geocoded addresses to the dfs_rich
dfs_rich2 = dfs_rich.merge(venue_addresses,how='left',left_on='place_address',right_on='address_prompt')
dfs_rich2.loc[dfs_rich2['place_address'].str.contains('University of South Australia',na=False),'location':] = np.nan # fix one address
# print(f'{round(dfs_rich2[dfs_rich2["latitude2"].notnull()].shape[0]/dfs_rich2.shape[0],2)*100}% of the events could be geocoded.')

dfs_rich_uncoded = dfs_rich2[(dfs_rich2["latitude2"].isnull()) | (dfs_rich2["address_prompt"].isnull())].copy()
dfs_rich_coded = dfs_rich2[(dfs_rich2["latitude2"].notnull()) & (dfs_rich2["address_prompt"].notnull())].copy()
# dfs_rich_coded = dfs_rich2[(dfs_rich2["latitude2"].notnull()) & (dfs_rich2["address_prompt"].notnull())].copy()[['place_address',
#                                                                      'decade_start',
#                                                                      'latitude','longitude',
#                                                                      'address_prompt',
#                                                                      'location','point',
#                                                                      'latitude2','longitude2']]

# keep rows with Frequency over 4
dfs_rich_coded_counts = dfs_rich_coded[['point']].value_counts().reset_index().rename(columns={0:'Frequency'})
unique_points = dfs_rich_coded_counts[dfs_rich_coded_counts['Frequency'] > 4]['point'].values
dfs_rich_coded_top = dfs_rich_coded[dfs_rich_coded['point'].isin(unique_points)].copy()

# keep rows with Frequency over 4
dfs_rich_uncoded_counts = dfs_rich_uncoded[['latitude','longitude']].value_counts().reset_index().rename(columns={0:'Frequency'})
unique_longlats = dfs_rich_uncoded_counts[dfs_rich_uncoded_counts['Frequency'] > 4][['latitude','longitude']].values
dfs_rich_uncoded_top = dfs_rich_uncoded[dfs_rich_uncoded['latitude'].isin(unique_longlats[:,0]) & dfs_rich_uncoded['longitude'].isin(unique_longlats[:,1])].copy()

# pre-established dictionary of geocodes

responses = {(-33.867265200000006, 151.21301147249176, 0.0): '1',
 (-12.4372686, 130.8332833, 0.0): '1',
 (-33.86882365, 151.21727034707598, 0.0): '1',
 (-33.8694899, 151.2206907, 0.0): '1',
 (-34.9239878, 138.6104338, 0.0): '1',
 (-34.920630700000004, 138.60396883837132, 0.0): '1',
 (-37.82266345, 144.96892412239615, 0.0): '1',
 (-35.3008452, 149.13668506334574, 0.0): '1',
 (-36.75734815, 144.27683511891263, 0.0): '1',
 (-33.7725373, 151.1151231, 0.0): '1',
 (-33.798468, 151.2812893, 0.0): '1',
 (-33.9494218, 150.9128418, 0.0): '1',
 (-33.8600913, 151.2089506947579, 0.0): '1',
 (-27.4729293, 153.01808630436386, 0.0): '1',
 (-33.8789196, 151.1993556, 0.0): '1',
 (-37.797314650000004, 144.9823961747878, 0.0): '1',
 (-27.472575, 153.01856075208468, 0.0): '1',
 (-33.8826261, 151.2340556, 0.0): '1',
 (-37.8046748, 144.9714691146325, 0.0): '1',
 (-33.863764149999994, 151.2114793699776, 0.0): '1',
 (-37.8266419, 144.9668874, 0.0): '1',
 (-30.525260250000002, 151.66486293938715, 0.0): '1',
 (-33.2813301, 149.1034393, 0.0): '1',
 (-34.07272735, 150.8087803136764, 0.0): '1',
 (-33.8838375, 151.2202731177872, 0.0): '1',
 (-35.32073065, 149.0944464686902, 0.0): '1',
 (-33.75838175, 150.66455904439798, 0.0): '1',
 (-33.8613912, 151.2042793, 0.0): '1',
 (-33.82619845, 151.24050182530272, 0.0): '1',
 (-38.1471881, 144.3569601, 0.0): '1',
 (-27.459477, 153.036797, 0.0): '1',
 (-27.54662495, 153.06429222548272, 0.0): '1',
 (-37.9077439, 145.1902768, 0.0): '1',
 (-31.97688685, 115.81616536223873, 0.0): '1',
 (-37.8090856, 144.9639910564144, 0.0): '1',
 (-33.87878675, 151.2163986873104, 0.0): '1',
 (-37.865052, 144.976647, 0.0): '1',
 (-37.8771439, 145.0460965, 0.0): '1',
 (-34.03312295, 151.08224418940046, 0.0): '1',
 (-37.7586271, 145.0842075, 0.0): '1',
 (-33.42289495, 149.57935342500002, 0.0): '1',
 (-42.8819659, 147.33201014693918, 0.0): '1',
 (-33.8685079, 151.2167508, 0.0): '1',
 (-37.56045175, 143.85840343225317, 0.0): '1',
 (-35.29982495, 149.13387409099266, 0.0): '1',
 (-38.3862623, 142.4822181, 0.0): '1',
 (-33.8668052, 151.21282231178583, 0.0): '1',
 (-33.879796150000004, 151.21851082933193, 0.0): '1',
 (31.1160029, 121.0486807, 0.0): '1',
 (-38.2368308, 146.3942691983112, 0.0): '1',
 (-37.8038271, 144.9597056360034, 0.0): '1',
 (-33.7073783, 150.3759584, 0.0): '1',
 (-27.4765707, 153.0288645, 0.0): '1',
 (-27.4600708, 153.0146024, 0.0): '1'}

verified_df = pd.DataFrame()
# responses = dict()
response = None

for idx, row in enumerate(dfs_rich_coded_top.point.unique()):
    thisrow = dfs_rich_coded_top[dfs_rich_coded_top.point == row].copy()
    if eval(row) in responses: verified_df = pd.concat([verified_df,thisrow],axis=0)

    # first time use
    # if row not in responses:
    #     print(thisrow['address_prompt'].value_counts().reset_index().rename(columns={'index':'address_prompt','address_prompt':'count'}).to_string(), '\n')
    #     response = input()
    #     if response == '1': 
    #         verified_df = pd.concat([verified_df,thisrow],axis=0)
    #         responses[row] = response
    #     elif response == 'q': break
    #     else: pass

    # if response == 'q': break

# create a new dataframe for each unique address with matches
verified_df1 = pd.DataFrame()

for thispoint in dfs_rich_coded_top[dfs_rich_coded_top.index.isin(verified_df.index)]['point'].unique():
    # get all rows with address in all_addresses
    thisrow = dfs_rich_coded_top[dfs_rich_coded_top['point'] == thispoint].copy()

    # get the most common response
    thisrow['address_prompt'] = thisrow['address_prompt'].value_counts().index[0]

    # concat to verified2
    verified_df1 = pd.concat([verified_df1,thisrow],axis=0)

verified_df1 = verified_df1.drop_duplicates()
print('Below are the top 10 most common place names for the verified addresses (after cleaning part 1): \n')
verified_df1['address_prompt'].value_counts().head(10)

Below are the top 10 most common place names for the verified addresses (after cleaning part 1): 

Art Gallery of New South Wales, Sydney, NSW     267
Institute of Modern Art, Brisbane, QLD          214
Queensland Art Gallery, Brisbane, QLD           112
National Gallery of Victoria, Melbourne, VIC    107
Warrnambool Art Gallery, Warrnambool, VIC       100
Orange Regional Gallery, Orange, NSW             99
Art Gallery of Western Australia, Perth, WA      92
Ballarat Fine Art Gallery, Ballarat, VIC         82
Artspace, Sydney, NSW                            80
Heide Museum of Modern Art, Melbourne, VIC       72
Name: address_prompt, dtype: int64

Cleaning by edit distance

# use pairwise distancing to find similar addresses using only address_prompt
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.feature_extraction.text import TfidfVectorizer

unique_addresses = dfs_rich_uncoded_top[dfs_rich_uncoded_top['address_prompt'].notnull()]['address_prompt'].unique()

# conver to lower case
unique_addresses2 = [x.lower() for x in unique_addresses]

# remove commas and periods
unique_addresses2 = [x.replace(',','') for x in unique_addresses2]
unique_addresses2 = [x.replace('.','') for x in unique_addresses2]

# remove_these_terms = ['nsw', 'sydney', 'melbourne', 'vic', 'qld', 'brisbane', 
#                       'adelaide', 'new south wales', 'perth', 'canberra', 'australia', 'paddington',
#                       'tas','act','queensland','hobart']

# remove strings in this list, remove_these_terms
# unique_addresses2 = [' '.join([word for word in x.split() if word not in remove_these_terms]) for x in unique_addresses2]

unique_addresses = pd.DataFrame([unique_addresses, unique_addresses2]).T

tfidf = TfidfVectorizer()
tfidf_matrix = tfidf.fit_transform(unique_addresses2)
distances = pairwise_distances(tfidf_matrix, metric='cosine')

# get pairs with threshold less than 0.5
pairs = np.argwhere(distances < 0.25)
pairs = [(unique_addresses2[x[0]], unique_addresses2[x[1]]) for x in pairs]

# get unique pairs
unique_pairs = []
for pair in pairs:
    if pair[0] != pair[1] and pair not in unique_pairs and (pair[1], pair[0]) not in unique_pairs:
        unique_pairs.append(pair)

# print(len(unique_pairs))

unique_pairs_df = pd.DataFrame(unique_pairs)
unique_pairs_df.columns = ['address1','address2']

# create a new dataframe for each unique address1
# each unique address1 should have a corresponding list of address2s

# create a dictionary of address1 and address2s
address_dict = dict()
for idx, row in unique_pairs_df.iterrows():
    if row['address1'] not in address_dict: address_dict[row['address1']] = [row['address2']]
    else: address_dict[row['address1']].append(row['address2'])

# create a dataframe of address1 and address2s
address_df = pd.DataFrame.from_dict(address_dict, orient='index').reset_index()

# rename columns
address_df.columns = ['address1','address2','address3','address4','address5']

# merge with unique_addresses
address_df = unique_addresses.merge(address_df, left_on=1, right_on='address1', how='left')
address_df['all_addresses'] = address_df.apply(lambda x: [x['address1'], x['address2'], x['address3'], x['address4'], x['address5']], axis=1)
address_df['all_addresses'] = address_df['all_addresses'].apply(lambda x: [y for y in x if str(y) != 'nan'])
address_df['all_addresses'] = address_df['all_addresses'].apply(lambda x: list(set(x)))

address_dict = dict()
for idx, row in address_df.iterrows(): address_dict[row[1]] = row[0]

# replace all data from address1 to address5 according to address_dict
for idx, row in address_df.iterrows():
    for col in ['address1','address2','address3','address4','address5']:
        if row[col] is not None:
            if str(row[col]) != 'nan': address_df.loc[idx,col] = address_dict[row[col]]

# create a list column in address_df which stores all data from address1 to address5 without nulls
address_df['all_addresses'] = address_df.apply(lambda x: [x['address1'], x['address2'], x['address3'], x['address4'], x['address5']], axis=1)
address_df['all_addresses'] = address_df['all_addresses'].apply(lambda x: [y for y in x if y is not None])
address_df['all_addresses'] = address_df['all_addresses'].apply(lambda x: [y for y in x if str(y) != 'nan'])
address_df = address_df[[0,'all_addresses']]

# create a new dataframe for each unique address with matches
verified_df2 = pd.DataFrame()

for idx,row in address_df.iterrows():
    if len(row['all_addresses']) > 0:
        # get all rows with address in all_addresses
        thisrow = dfs_rich_uncoded_top[dfs_rich_uncoded_top['address_prompt'].isin(row['all_addresses'])].copy()

        if thisrow.shape[0] > 4:
            # get the most common response
            thisrow['address_prompt'] = thisrow['address_prompt'].value_counts().index[0]

            # concat to verified2
            verified_df2 = pd.concat([verified_df2,thisrow],axis=0)

# verified_df2 = verified_df2.drop_duplicates()

# merge back with original dataframe
v_df2_cols = dfs_rich_uncoded_top.columns.tolist()
v_df2_cols.remove('address_prompt')
verified_df2 = pd.concat([dfs_rich_uncoded_top, verified_df2],axis=0).drop_duplicates(subset=v_df2_cols)

# get the most common response
print('Below are the top 10 most common place names for the verified addresses (after cleaning part 2):: \n')
verified_df2['address_prompt'].value_counts().head(10)

Below are the top 10 most common place names for the verified addresses (after cleaning part 2):: 

Experimental Art Foundation, Adelaide, SA                             425
Contemporary Art Centre of South Australia, Adelaide, SA              342
Wollongong City Gallery, Wollongong, NSW                              166
Museums and Art Galleries of the Northern Territory, Darwin, NT       121
8 Llankelly Place, Kings Cross, Sydney, 2011                           98
Newcastle Region Art Gallery, Newcastle, NSW                           96
Tin Sheds Gallery, University of Sydney, Sydney, NSW                   79
Queen Victoria Museum and Art Gallery, Launceston, TAS                 79
Brisbane City Art Gallery, Brisbane, Qld                               68
Ian Potter Museum of Art, University of Melbourne, Melbourne, Vic.     64
Name: address_prompt, dtype: int64

Final cleaning phase

The final phase consists of merging the two cleaned datasets, and removing any duplicate venues. We also remove any venues with less than 5 exhibitions. The final data consists of 43% of data modified by geocode aggregation and 57% of data modified by edit distance.

verified_df1['coded'] = True
verified_df2['coded'] = False

clean_data_v1 = pd.concat([verified_df1,verified_df2],axis=0)
clean_data_v1 = clean_data_v1.drop_duplicates(subset=v_df2_cols)

# use pairwise distancing to find similar addresses using only address_prompt
unique_addresses = clean_data_v1[clean_data_v1['address_prompt'].notnull()]['address_prompt'].unique()

# conver to lower case
unique_addresses2 = [x.lower() for x in unique_addresses]

# remove commas and periods
unique_addresses2 = [x.replace(',','') for x in unique_addresses2]
unique_addresses2 = [x.replace('.','') for x in unique_addresses2]
unique_addresses = pd.DataFrame([unique_addresses, unique_addresses2]).T

tfidf = TfidfVectorizer()
tfidf_matrix = tfidf.fit_transform(unique_addresses2)
distances = pairwise_distances(tfidf_matrix, metric='cosine')

# get pairs with threshold less than 0.5
pairs = np.argwhere(distances < 0.15)
pairs = [(unique_addresses2[x[0]], unique_addresses2[x[1]]) for x in pairs]

# get unique pairs
unique_pairs = []
for pair in pairs:
    if pair[0] != pair[1] and pair not in unique_pairs and (pair[1], pair[0]) not in unique_pairs:
        unique_pairs.append(pair)

# print(len(unique_pairs))

unique_pairs_df = pd.DataFrame(unique_pairs)
unique_pairs_df.columns = ['address1','address2']

# create a new dataframe for each unique address1
# each unique address1 should have a corresponding list of address2s

# create a dictionary of address1 and address2s
address_dict = dict()
for idx, row in unique_pairs_df.iterrows():
    if row['address1'] not in address_dict: address_dict[row['address1']] = [row['address2']]
    else: address_dict[row['address1']].append(row['address2'])

# create a dataframe of address1 and address2s
address_df = pd.DataFrame.from_dict(address_dict, orient='index').reset_index()

# rename columns
address_df.columns = ['address1','address2','address3','address4','address5']

# merge with unique_addresses
address_df = unique_addresses.merge(address_df, left_on=1, right_on='address1', how='left')
address_df['all_addresses'] = address_df.apply(lambda x: [x['address1'], x['address2'], x['address3'], x['address4'], x['address5']], axis=1)
address_df['all_addresses'] = address_df['all_addresses'].apply(lambda x: [y for y in x if str(y) != 'nan'])
address_df['all_addresses'] = address_df['all_addresses'].apply(lambda x: list(set(x)))

address_dict = dict()
for idx, row in address_df.iterrows(): address_dict[row[1]] = row[0]

# replace all data from address1 to address5 according to address_dict
for idx, row in address_df.iterrows():
    for col in ['address1','address2','address3','address4','address5']:
        if row[col] is not None:
            if str(row[col]) != 'nan': address_df.loc[idx,col] = address_dict[row[col]]

# create a list column in address_df which stores all data from address1 to address5 without nulls
address_df['all_addresses'] = address_df.apply(lambda x: [x['address1'], x['address2'], x['address3'], x['address4'], x['address5']], axis=1)
address_df['all_addresses'] = address_df['all_addresses'].apply(lambda x: [y for y in x if y is not None])
address_df['all_addresses'] = address_df['all_addresses'].apply(lambda x: [y for y in x if str(y) != 'nan'])
address_df = address_df[[0,'all_addresses']]

# update dataframe with new address_prompt
clean_data_v2 = clean_data_v1.copy()

for idx,row in address_df.iterrows():
    if len(row['all_addresses']) > 0:
        # get all rows with address in all_addresses
        thisrow = clean_data_v2[clean_data_v2['address_prompt'].isin(row['all_addresses'])].copy()

        if thisrow.shape[0] > 4:
            # get the most common response
            clean_data_v2.loc[clean_data_v2['address_prompt'].isin(row['all_addresses']),'address_prompt'] = thisrow['address_prompt'].value_counts().index[0]

# merge back with original dataframe
clean_data_v2 = pd.concat([clean_data_v1, clean_data_v2],axis=0).drop_duplicates(subset=v_df2_cols)
clean_data_v2 = clean_data_v2[clean_data_v2['address_prompt'].isin(clean_data_v2['address_prompt'].value_counts()[clean_data_v2['address_prompt'].value_counts() > 4].index)]
clean_data_v2 = clean_data_v2.drop_duplicates()
print('Below are the top 20 most common place names for the verified addresses (after final cleaning): \n')
clean_data_v2.address_prompt.value_counts().head(20)

Below are the top 20 most common place names for the verified addresses (after final cleaning): 

Experimental Art Foundation, Adelaide, SA                          425
Contemporary Art Centre of South Australia, Adelaide, SA           342
Art Gallery of New South Wales, Sydney, NSW                        267
Institute of Modern Art, Brisbane, QLD                             214
Wollongong City Gallery, Wollongong, NSW                           166
Museums and Art Galleries of the Northern Territory, Darwin, NT    121
Queensland Art Gallery, Brisbane, QLD                              112
National Gallery of Victoria, Melbourne, VIC                       107
Warrnambool Art Gallery, Warrnambool, VIC                          100
Orange Regional Gallery, Orange, NSW                                99
8 Llankelly Place, Kings Cross, Sydney, 2011                        98
Newcastle Region Art Gallery, Newcastle, NSW                        96
Art Gallery of Western Australia, Perth, WA                         92
Ballarat Fine Art Gallery, Ballarat, VIC                            82
Artspace, Sydney, NSW                                               80
Queen Victoria Museum and Art Gallery, Launceston, TAS              79
Tin Sheds Gallery, University of Sydney, Sydney, NSW                79
Heide Museum of Modern Art, Melbourne, VIC                          72
Brisbane City Art Gallery, Brisbane, Qld                            68
Art Gallery of South Australia, Adelaide, SA                        66
Name: address_prompt, dtype: int64

Compare this with original table:

Venue

City

Frequency

Art Gallery of New South Wales

Sydney, NSW

321

Wollongong City Gallery

Wollongong, NSW

192

Museums and Art Galleries of the Northern Territory

Darwin, NT

152

National Gallery of Victoria

Melbourne, VIC

134

Queensland Art Gallery

Brisbane, QLD

133

Newcastle Region Art Gallery

Newcastle, NSW

126

Orange Regional Gallery

Orange, NSW

109

Warrnambool Art Gallery

Warrnambool, VIC

108

Art Gallery of Western Australia

Perth, WA

106

8 Llankelly Place, Kings Cross

Sydney, NSW

103

Most frequent terms used for place names

From here, we explore the most frequent terms used for place names. We find that the most frequent terms are “Gallery”, “Art”, “Centre”, “Museum”, and “University”.

# find the most frequent words across the strings for place names
from collections import Counter
import matplotlib.pyplot as plt

import nltk
# nltk.download('punkt')

from nltk.tokenize import word_tokenize

# create a list of all the place names
all_place_names = clean_data_v2['address_prompt'].astype(str).tolist()

# create a list of all the words in the place names
all_words = []
for place_name in all_place_names:
    all_words.extend(word_tokenize(place_name))

# find top 100 most frequent words
most_freq_words = Counter(all_words).most_common(85)

# remove a list of all words that are not relevant
words_to_remove = [',','NSW','Sydney','Melbourne','Adelaide','South','SA','Brisbane','VIC',
                   '.', 'New', 'Australia', 'QLD', 'Vic', 'Wales', 'WA', 'Canberra', 'and',
                   'Perth', 'ACT', 'of', 'Qld', 'Victoria','Wollongong','TAS','Queensland','Newcastle',
                   'Street','Hobart','the','The','Launceston','Orange','UK','NT','London','USA',
                   'Paddington','Darwin','for','Western','Warrnambool','Ballarat','Northern','Territory',
                   'England','Watters','Macquarie','Artspace','St',"'s",'&','Potter','Kings','Ian','Cross',
                   '8','Llankelly','2011','Fremantle','Queen','Ivan','Dougherty','Tasmania','Central','Tamar',
                   'Curtin','France','Tin','Sheds','York','Monash','Paris','Heide','Tasmanian','Sherman','Campbelltown']

# remove the words from the list of most frequent words
most_freq_words = [word for word in most_freq_words if word[0] not in words_to_remove]

most_freq_words_dict = dict(most_freq_words)

# add value of two keys
most_freq_words_dict['Gallery'] = most_freq_words_dict['Gallery'] + most_freq_words_dict['Galleries']
# most_freq_words_dict['Museum'] = most_freq_words_dict['Museum'] + most_freq_words_dict['Museums']

# remove key 'Gallery'
most_freq_words_dict.pop('Galleries')
# most_freq_words_dict.pop('Museums')
most_freq_words_dict2 = most_freq_words_dict.copy()

# create a wordcloud with the most frequent words
from wordcloud import WordCloud

wordcloud = WordCloud(width = 800, height = 800,
                background_color ='white',
                min_font_size = 10).generate_from_frequencies(most_freq_words_dict)

# plot the WordCloud image
plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)

plt.show()
_images/DAAO_Venues_18_0.png

Hierarchical clustering using event description data

Next we explore exhibition descriptions using hierarchical clustering; a method used to group similar objects into clusters that follow a hierarchical structure. This can help conceptualise what a taxonomy of venue categories might look like. We use Google’s BERT to embed the data.

Hierarchical clustering - data pre-processing

import seaborn as sns
import re
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, fcluster

# import nltk
# nltk.download('stopwords')
from nltk.corpus import stopwords

### collect all relevant textual data from the dataframe
# if description is not \n then append to slug2 in one line
clean_data_v2['slug2'] = clean_data_v2['title'].fillna('') + clean_data_v2['description'].apply(lambda x: '' if x == '\n' else x)

### pre-process for NLP
# Load the documents and their corresponding categorical variables into a Pandas dataframe
df = pd.DataFrame({'text': clean_data_v2['slug2'], 'category': clean_data_v2['address_prompt']})

# summarise text for each unique place name
df['text'] = df.groupby('category')['text'].transform(lambda x: ' '.join(x))

#add new column with count for each category
df['cat_count'] = df.groupby('category')['category'].transform('count')
df.drop_duplicates(inplace=True)

# Clean the text
stop_words = set(stopwords.words('english'))

def clean_text(text):
    text = re.sub('[^a-zA-Z]', ' ', text)
    text = text.replace(',', '')
    text = text.lower().split()
    text = [word for word in text if word not in stop_words]
    text = ' '.join(text)
    return text

df = df[df['text'].notnull()]
df['clean_text'] = df['text'].apply(clean_text)

### Generate clusters

def get_most_common_word(df, more_words = [], no_of_words=25):
    # remove a list of all words that are not relevant
    # words_to_remove = [',','NSW','Sydney','Melbourne','Adelaide','South','SA','Brisbane','VIC',
    #                 '.', 'New', 'Australia', 'QLD', 'Vic', 'Wales', 'WA', 'Canberra', 'and',
    #                 'Perth', 'ACT', 'of', 'Qld', 'Victoria','Wollongong','TAS','Queensland','Newcastle',
    #                 'Street','Hobart','the','The','Launceston','Orange','UK','NT','London','USA',
    #                 'Paddington','Darwin','for','Western','Warrnambool','Ballarat','Northern','Territory',
    #                 'England','Watters','Macquarie','Artspace','St',"'s",'&','Potter','Kings','Ian','Cross',
    #                 '8','Llankelly','2011','Fremantle','Queen','Ivan','Dougherty','Tasmania','Central',
    #                 'Curtin','France','Tin','Sheds','York','Monash','Paris','Heide','Place','Vic.',]
    
    words_to_remove = []
    
    # add more words to the list of words to remove
    words_to_remove = words_to_remove + more_words

    all_words = []
    for i in df:
        for j in i.split(' '):
            all_words.append(j.replace(',',''))

    # find the most common words
    most_common_words = Counter(all_words).most_common(no_of_words)

    # remove the words from the list of most frequent words
    most_common_words = [word for word in most_common_words if word[0] not in words_to_remove]
    return most_common_words

def get_linkage_matrix(model, **kwargs):
    # Create linkage matrix and then plot the dendrogram

    # create the counts of samples under each node
    counts = np.zeros(model.children_.shape[0])
    n_samples = len(model.labels_)
    for i, merge in enumerate(model.children_):
        current_count = 0
        for child_idx in merge:
            if child_idx < n_samples:
                current_count += 1  # leaf node
            else:
                current_count += counts[child_idx - n_samples]
        counts[i] = current_count

    linkage_matrix = np.column_stack(
        [model.children_, model.distances_, counts]
    ).astype(float)

    return linkage_matrix

Hierarchical clustering (BERT)

We repeat the methodology but now change the embedding method to BERT. We expect using BERT, a much more complex model, to better represent each venue. We find that the clusters are more widely populated, and output a different outcome.

# ### load bert model

# # !pip install transformers
# from transformers import BertTokenizer, BertModel
# from transformers import pipeline
# tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# model = BertModel.from_pretrained("bert-base-uncased")

# ### encode text using bert (takes 15 seconds to run)
# import random

# # encode the text using bert
# def bert_encode(x):
#   encoded_input = tokenizer(x, return_tensors='pt')
#   output = model(**encoded_input)
#   return pd.DataFrame(output['pooler_output'].detach().numpy()).T

# # randomly sample 512 tokens from each row in df['clean_text']
# # some strings are smalle than 512
# df['clean_text_sampled'] = df['clean_text'].apply(lambda x: ' '.join(random.sample(x.split(' '), 300)) if len(x.split(' ')) >= 300 else x)
# X_bert = df['clean_text_sampled'].apply(lambda x: pd.Series(bert_encode([str(x)])[0]))

# # setting distance_threshold=0 ensures we compute the full tree.
# model_bert = AgglomerativeClustering(distance_threshold=0, n_clusters=None)
# model_bert = model_bert.fit(np.array(X_bert))

# # save model as pickle
# pickle.dump(model_bert, open('models/model_bert.pkl', 'wb'))

from textwrap import wrap

# read model from pickle
import pickle
model_bert = pickle.load(open('models/model_bert.pkl', 'rb'))

# if a word is a duplicate then remove from all_words
def find_duplicates(all_words, occurences=1):
    duplicates = []
    non_duplicates = []

    for i in all_words:
        if i in duplicates: continue
        else:
            if all_words.count(i) > occurences: duplicates.append(i)
            else: non_duplicates.append(i)
    return duplicates

cut = 7
l_matrix = get_linkage_matrix(model_bert)
df['cluster'] = fcluster(l_matrix, cut, criterion='maxclust')
dendrogram(l_matrix, orientation='top', truncate_mode="lastp", p=cut, show_leaf_counts=True)

all_words = []

for i in df['cluster'].unique():
    cluster_docs = df[df['cluster'] == i]
    # print(i, get_most_common_word(cluster_docs['clean_text']))

    annot = "\n".join(i[0] for idx,i in enumerate(get_most_common_word(cluster_docs['clean_text'])) if (idx < 3))
    
    plt.annotate(annot, xy=(i/df['cluster'].nunique()-0.1, 0.15), 
                 xytext=(i/df['cluster'].nunique()-0.1, 0.15), 
                 xycoords='axes fraction', fontsize=9, color='red')

    [all_words.append(i[0]) for idx,i in enumerate(get_most_common_word(cluster_docs['clean_text']))]
    
all_words_to_remove = find_duplicates(all_words, occurences=2)
all_words_to_remove.extend(['j','th','nd','exhibitionexhibited','http','www','isbn'])

for i in df['cluster'].unique():
    cluster_docs = df[df['cluster'] == i]
    # print(i, get_most_common_word(cluster_docs['clean_text']))
    annot = "\n".join(i[0] for idx,i in enumerate(get_most_common_word(cluster_docs['clean_text'],
                                                                       more_words=all_words_to_remove)) if (idx < 5))
    
    plt.annotate(annot, xy=(i/df['cluster'].nunique()-0.1, 0.025), 
                 xytext=(i/df['cluster'].nunique()-0.1, 0.025), 
                 xycoords='axes fraction', fontsize=9)
    
    annot2 = cluster_docs.sort_values('cat_count', ascending=False)['category'].values[0:3]
    annot2 = '\n\n'.join(['\n'.join(wrap(line, 18)) for line in [i.split(',')[0] for i in annot2]])
    # annot2 = '\n'.join(wrap(annot2, 18)) # breaks strings into new lines

    plt.annotate(annot2, xy=(i/df['cluster'].nunique()-0.115, -0.24), 
                xytext=(i/df['cluster'].nunique()-0.115, -0.24), 
                xycoords='axes fraction', fontsize=9)

plt.title("Hierarchical Clustering Dendrogram - BERT")

# make figure bigger
fig = plt.gcf()
fig.set_size_inches(14, 10)

plt.show()

# # save the figure
# fig.savefig('images/daao_tlc/outputnew_bert.png', dpi=300, bbox_inches='tight')

# from IPython.display import Image
# Image(filename='images/daao_tlc/outputnew_bert.png')

# # df to xlsx
# df.to_excel('data/142_venues_v1.xlsx', index=False)
_images/DAAO_Venues_24_0.png

We provide the same visual however show the most frequent terms used within the place names as opposed to the event descriptions.

cut = 7
l_matrix = get_linkage_matrix(model_bert)
df['cluster'] = fcluster(l_matrix, cut, criterion='maxclust')
dendrogram(l_matrix, orientation='top', truncate_mode="lastp", p=cut, show_leaf_counts=True)

all_words = []

for i in df['cluster'].unique():
    cluster_docs = df[df['cluster'] == i]
    # print(i, get_most_common_word(cluster_docs['clean_text']))

    annot = "\n".join(i[0] for idx,i in enumerate(get_most_common_word(cluster_docs['category'])) if (idx < 3))
    
    plt.annotate(annot, xy=(i/df['cluster'].nunique()-0.1, 0.15), 
                 xytext=(i/df['cluster'].nunique()-0.1, 0.15), 
                 xycoords='axes fraction', fontsize=9, color='red')
    
    [all_words.append(i[0]) for idx,i in enumerate(get_most_common_word(cluster_docs['category'], no_of_words=25))]
    
all_words_to_remove = find_duplicates(all_words, occurences=4)
all_words_to_remove.extend([',','NSW','Sydney','Melbourne','Adelaide','South','SA','Brisbane','VIC',
                   '.', 'New', 'Australia', 'QLD', 'Vic','and','WA','Victoria','ACT','Qld',
                   'of','Wollongong','TAS','Queensland','Newcastle',
                   'Street','Hobart','the','The','Launceston','Orange','NT',
                   'Paddington','Darwin','for','Western','Warrnambool','Ballarat','Northern','Territory',
                   'England','Watters','Macquarie','Artspace','St',"'s",'&','Potter','Kings','Ian','Cross',
                   '8','Llankelly','2011','Fremantle','Queen','Ivan','Dougherty','Tasmania','Central',
                   'Curtin','France','Tin','Sheds','York','Monash','Heide',''])

for i in df['cluster'].unique():
    cluster_docs = df[df['cluster'] == i]
    # print(i, get_most_common_word(cluster_docs['clean_text']))
    annot = "\n".join(i[0] for idx,i in enumerate(get_most_common_word(cluster_docs['category'],
                                                                       more_words=all_words_to_remove)) if (idx < 5))
    
    plt.annotate(annot, xy=(i/df['cluster'].nunique()-0.1, 0.05), 
                 xytext=(i/df['cluster'].nunique()-0.1, 0.05), 
                 xycoords='axes fraction', fontsize=9)

plt.title("Hierarchical Clustering Dendrogram - BERT")

# make figure bigger
fig = plt.gcf()
fig.set_size_inches(14, 10)

plt.show()

# # save the figure
# fig.savefig('images/daao_tlc/outputnew_bert_placename.png', dpi=300, bbox_inches='tight')

# from IPython.display import Image
# Image(filename='images/daao_tlc/outputnew_bert_placename.png')
_images/DAAO_Venues_26_0.png

Hierarchy Clustering - BERT by demi-decade

Using the BERT model, we generate a series of dendrograms that show the hierarchical clustering of the exhibition descriptions by 5-year periods. We omit pre-1970s data as there is not enough data to generate a meaningful dendrogram for each demi-decade (see table output below).

clean_data_v2['decade_start'].value_counts().sort_index().reset_index().rename(columns={'index':'decade_start', 'decade_start':'count'}).T
0 1 2 3 4 5 6 7 8 9 10 11 12
decade_start 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
count 7 6 4 5 13 35 73 506 1030 1464 1064 207 1

Top ten place names and year

clean_data_v2[['start_year','address_prompt']].value_counts().head(10)

start_year  address_prompt                                          
1983.0      Experimental Art Foundation, Adelaide, SA                   45
1982.0      Experimental Art Foundation, Adelaide, SA                   30
1979.0      Contemporary Art Centre of South Australia, Adelaide, SA    25
1978.0      Contemporary Art Centre of South Australia, Adelaide, SA    23
1986.0      Experimental Art Foundation, Adelaide, SA                   23
1979.0      Experimental Art Foundation, Adelaide, SA                   22
1997.0      Experimental Art Foundation, Adelaide, SA                   22
1976.0      Experimental Art Foundation, Adelaide, SA                   22
1985.0      Experimental Art Foundation, Adelaide, SA                   22
1981.0      Experimental Art Foundation, Adelaide, SA                   20
dtype: int64

Top ten place names and decade

clean_data_v2[['decade_start','address_prompt']].value_counts().head(10)

decade_start  address_prompt                                                 
1980          Experimental Art Foundation, Adelaide, SA                          199
1970          Contemporary Art Centre of South Australia, Adelaide, SA           110
1980          Contemporary Art Centre of South Australia, Adelaide, SA           105
1990          Experimental Art Foundation, Adelaide, SA                          102
1970          Experimental Art Foundation, Adelaide, SA                           98
1990          Art Gallery of New South Wales, Sydney, NSW                         98
              Contemporary Art Centre of South Australia, Adelaide, SA            97
              Institute of Modern Art, Brisbane, QLD                              95
1980          Museums and Art Galleries of the Northern Territory, Darwin, NT     79
1990          Wollongong City Gallery, Wollongong, NSW                            74
dtype: int64

def temporal_dendos(startdt=1900, enddt=1920, cut_input=3, model=None):
    df_period = clean_data_v2[(clean_data_v2['start_year'] >= startdt) & (clean_data_v2['start_year'] <= enddt)]
    # df_period = clean_data_v2[(clean_data_v2['decade_start'] >= startdt) & (clean_data_v2['decade_start'] <= enddt)]
    ### pre-process for NLP
    # Load the documents and their corresponding categorical variables into a Pandas dataframe
    df_period = pd.DataFrame({'text': df_period['slug2'], 'category': df_period['address_prompt']})

    # summarise text for each unique place name
    df_period['text'] = df_period.groupby('category')['text'].transform(lambda x: ' '.join(x))

    #add new column with count for each category
    df_period['cat_count'] = df_period.groupby('category')['category'].transform('count')
    df_period.drop_duplicates(inplace=True)

    # Clean the text
    stop_words = set(stopwords.words('english'))
    df_period = df_period[df_period['text'].notnull()]
    df_period['clean_text'] = df_period['text'].apply(clean_text)

    if model is None:
        # randomly sample 512 tokens from each row in df['clean_text']
        # some strings are smalle than 512
        df_period['clean_text_sampled'] = df_period['clean_text'].apply(lambda x: ' '.join(random.sample(x.split(' '), 275)) if len(x.split(' ')) >= 275 else x)
        X_bert_period = df_period['clean_text_sampled'].apply(lambda x: pd.Series(bert_encode([str(x)])[0]))

        # setting distance_threshold=0 ensures we compute the full tree.
        model_bert_period = AgglomerativeClustering(distance_threshold=0, n_clusters=None)
        model_bert_period = model_bert_period.fit(np.array(X_bert_period))

        # save model as pickle
        pickle.dump(model_bert_period, open(f'models/model_bert_{startdt}_{enddt}.pkl', 'wb'))
    else:
        model_bert_period = pickle.load(open(f'models/model_bert_{startdt}_{enddt}.pkl', 'rb'))

    ### generate dendrogram
    cut = cut_input
    l_matrix = get_linkage_matrix(model_bert_period)
    df_period['cluster'] = fcluster(l_matrix, cut, criterion='maxclust')
    dendrogram(l_matrix, orientation='top', truncate_mode="lastp", p=cut, show_leaf_counts=True)

    all_words = []

    for i in df_period['cluster'].unique():
        cluster_docs = df_period[df_period['cluster'] == i]
        # print(i, get_most_common_word(cluster_docs['clean_text']))

        annot = "\n".join(i[0] for idx,i in enumerate(get_most_common_word(cluster_docs['clean_text'])) if (idx < 3))
        
        plt.annotate(annot, xy=(i/df_period['cluster'].nunique()-0.1, 0.15), 
                    xytext=(i/df_period['cluster'].nunique()-0.1, 0.15), 
                    xycoords='axes fraction', fontsize=9, color='red')

        [all_words.append(i[0]) for idx,i in enumerate(get_most_common_word(cluster_docs['clean_text']))]
        
    all_words_to_remove = find_duplicates(all_words, occurences=2)
    all_words_to_remove.extend(['j','th','nd','exhibitionexhibited','http','www','isbn'])

    for i in df_period['cluster'].unique():
        cluster_docs = df_period[df_period['cluster'] == i]
        # print(i, get_most_common_word(cluster_docs['clean_text']))
        annot = "\n".join(i[0] for idx,i in enumerate(get_most_common_word(cluster_docs['clean_text'],
                                                                        more_words=all_words_to_remove)) if (idx < 5))
        
        plt.annotate(annot, xy=(i/df_period['cluster'].nunique()-0.1, 0.025), 
                    xytext=(i/df_period['cluster'].nunique()-0.1, 0.025), 
                    xycoords='axes fraction', fontsize=9)
        
        annot2 = cluster_docs.sort_values('cat_count', ascending=False)['category'].values[0:3]
        annot2 = '\n\n'.join(['\n'.join(wrap(line, 18)) for line in [i.split(',')[0] for i in annot2]])
        # annot2 = '\n'.join(wrap(annot2, 18)) # breaks strings into new lines

        plt.annotate(annot2, xy=(i/df_period['cluster'].nunique()-0.115, -0.24), 
                    xytext=(i/df_period['cluster'].nunique()-0.115, -0.24), 
                    xycoords='axes fraction', fontsize=9)

    plt.title(f"Hierarchical Clustering Dendrogram - BERT - {startdt}-{enddt}")

    # make figure bigger
    fig = plt.gcf()
    fig.set_size_inches(14, 10)

    plt.show()
    return df_period

# from IPython.display import Image
# Image(filename='images/daao_tlc/outputnew_bert_1920_1940.png')

df_70_75 = temporal_dendos(1970, 1975, cut_input=5, model=0)
df_75_80 = temporal_dendos(1975, 1980, cut_input=5, model=0)
df_80_85 = temporal_dendos(1980, 1985, cut_input=5, model=0)
df_85_90 = temporal_dendos(1985, 1990, cut_input=5, model=0)
df_90_95 = temporal_dendos(1990, 1995, cut_input=5, model=0)
df_95_00 = temporal_dendos(1995, 2000, cut_input=5, model=0)
df_00_05 = temporal_dendos(2000, 2005, cut_input=5, model=0)
df_05_10 = temporal_dendos(2005, 2010, cut_input=5, model=0)
_images/DAAO_Venues_34_0.png _images/DAAO_Venues_34_1.png _images/DAAO_Venues_34_2.png _images/DAAO_Venues_34_3.png _images/DAAO_Venues_34_4.png _images/DAAO_Venues_34_5.png _images/DAAO_Venues_34_6.png _images/DAAO_Venues_34_7.png

Experimental Art Foundation, Adelaide, SA

df_test = pd.DataFrame()
venue = 'Experimental Art Foundation, Adelaide, SA'
df_test = try_merge(df_test, df_70_75, venue)
df_test = try_merge(df_test, df_75_80, venue)
df_test = try_merge(df_test, df_80_85, venue)
df_test = try_merge(df_test, df_85_90, venue)
df_test = try_merge(df_test, df_90_95, venue)
df_test = try_merge(df_test, df_95_00, venue)
df_test = try_merge(df_test, df_00_05, venue)
df_test = try_merge(df_test, df_05_10, venue)
print(df_test['category'].value_counts().head(6).tail(-1))

Art Gallery of New South Wales, Sydney, NSW           5
Queensland Art Gallery, Brisbane, QLD                 4
Art Gallery of Western Australia, Perth, WA           4
Corner Brisbane and Tamar Streets, Launceston, TAS    3
Art Gallery of South Australia, Adelaide, SA          3
Name: category, dtype: int64

Contemporary Art Centre of South Australia, Adelaide, SA

df_test = pd.DataFrame()
venue = 'Contemporary Art Centre of South Australia, Adelaide, SA'
df_test = try_merge(df_test, df_70_75, venue)
df_test = try_merge(df_test, df_75_80, venue)
df_test = try_merge(df_test, df_80_85, venue)
df_test = try_merge(df_test, df_85_90, venue)
df_test = try_merge(df_test, df_90_95, venue)
df_test = try_merge(df_test, df_95_00, venue)
df_test = try_merge(df_test, df_00_05, venue)
df_test = try_merge(df_test, df_05_10, venue)
print(df_test['category'].value_counts().head(6).tail(-1))

Monash University Museum of Art, Melbourne, VIC    5
Newcastle Region Art Gallery, Newcastle, NSW       5
National Gallery of Victoria, Melbourne, VIC       4
Heide Museum of Modern Art, Melbourne, VIC         4
Warrnambool Art Gallery, Warrnambool, VIC          4
Name: category, dtype: int64

Organisations

Similar to venues, we first produce a word cloud for common terms used in organisation names. We find that the most frequent terms are “Society”, “Art”, “Gallery”, “Royal”, and “Arts”.

# bios = pd.read_csv('data/daao_biography_level_202202211719.csv')
# bios = bios[['_id','primary_name.family_name','primary_name.given_names']]
# bios[bios['_id'].str.contains('4e1e1424b846f2dae7001dc0')]

with open("data/daao_persongroup_level.json") as json_file: daoo_org_data=json.load(json_file)

# create a list of all the place names
all_place_names = pd.json_normalize(daoo_org_data)['primary_name'].astype(str).tolist()

# create a list of all the words in the place names
all_words = []
for place_name in all_place_names:
    all_words.extend(word_tokenize(place_name))

# find top 100 most frequent words
most_freq_words = Counter(all_words).most_common(40)

# remove a list of all words that are not relevant
words_to_remove = [',','.','and','of','the','The','for',"'s",'&','(',')','J.','Melbourne',
                   'UK','VIC','London',
                   'Jane','Lapham','*','Adelaide',"'",'Sydney','NSW','New','South','Wales']

# remove the words from the list of most frequent words
most_freq_words = [word for word in most_freq_words if word[0] not in words_to_remove]

most_freq_words_dict = dict(most_freq_words)

# # add value of two keys
# most_freq_words_dict['Gallery'] = most_freq_words_dict['Gallery'] + most_freq_words_dict['Galleries']
# most_freq_words_dict['Museum'] = most_freq_words_dict['Museum'] + most_freq_words_dict['Museums']

# # remove key 'Gallery'
# most_freq_words_dict.pop('Galleries')
# most_freq_words_dict.pop('Museums')

# create a wordcloud with the most frequent words
from wordcloud import WordCloud

wordcloud = WordCloud(width = 800, height = 800,
                background_color ='white',
                min_font_size = 10).generate_from_frequencies(most_freq_words_dict)

# plot the WordCloud image
plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)

plt.show()
_images/DAAO_Venues_46_0.png

As shown above, the use of dendrograms can be an interesting exercise to explore the data. The initial idea was to repeat the same approach with organisations to assess any latent patterns through this taxonomic approrach. However, we find that the data is not as rich as the venue data and that there are many organisations with no biographies, no summaries, and no relations to people records, exhibition records, etc. Nevertheless we provide one dendrogram using organisation biographies.

Below is a list of the proportion of missing data for each field across DAAO organisations.

# find the proportion of null values in each column
print((pd.json_normalize(daoo_org_data).isnull().sum()/pd.json_normalize(daoo_org_data).shape[0])[0:20])

biography              0.869957
_cls                   0.000000
editing_complete       0.922318
is_deleted             0.000000
is_featured            0.000000
is_locked              0.000000
is_primary             0.000000
is_shadow              0.000000
primary_name           0.000858
periods_active         0.959227
record_status          0.000000
residences             0.959442
roles                  0.000000
sources                0.913519
slug                   0.000000
summary                0.917167
related_stub_people    0.998927
_id.$oid               0.000000
date_created.$date     0.024678
date_modified.$date    0.069528
dtype: float64

print((pd.json_normalize(daoo_org_data).isnull().sum()/pd.json_normalize(daoo_org_data).shape[0])[20:])

references                    0.896567
tags                          0.962017
type                          0.974464
types                         0.965665
related_events                0.972318
related_collections           0.985837
related_works                 0.922318
related_recognitions          0.996996
related_stub_person_groups    0.998927
copyright_agents              0.999571
urls                          0.896137
alternative_names             0.967811
locked_biographies            0.999356
other_occupations             0.996996
see_alsos                     0.982833
nla_id                        0.999356
source_database_ids           0.985837
dtype: float64

# from bs4 import BeautifulSoup

# orgs = pd.DataFrame(columns=['name','summary','bio'])

# # pd.json_normalize(daoo_org_data)['biography'].isnull().sum()/pd.json_normalize(daoo_org_data).shape[0] # 87% of rows have no biography

# for i,row in pd.json_normalize(daoo_org_data).iterrows():
#     try:
#         try: row['biography'][0]['text']
#         except: continue

#         # try: period_start = row['periods_active'][0]['start']['_date']
#         # except: period_start = None

#         # try: period_end = row['periods_active'][0]['end']['_date']
#         # except: period_end = None

#         # use pandas.concat to append new row to dataframe
#         orgs = pd.concat([orgs, pd.DataFrame({'name': [row['primary_name']],
#                                                 'summary': [row['summary']],
#                                                 'bio': [row['biography'][0]['text']],
#                                                 # 'period_start': [period_start],
#                                                 # 'period_end': [period_end]
#                                                 })], ignore_index=True)
        
#     except:
#         print(i)
#         break

# # remove empty summary
# orgs['summary'].fillna('', inplace=True)

# # remove rows with stub text
# orgs = orgs[~orgs['bio'].isin(orgs['bio'].value_counts().head(5).index.to_list())]

# # combine summary and bio
# orgs['bio'] = orgs['summary'] + orgs['bio'].apply(lambda x: BeautifulSoup(x, 'lxml').get_text())

# orgs = orgs[['name','bio']]

# ### encode text using bert (takes 4mins to run)

# ### pre-process for NLP
# # Load the documents and their corresponding categorical variables into a Pandas dataframe
# df_org = pd.DataFrame({'text': orgs['bio'], 'category': orgs['name']})

# #add new column with count for each category
# df_org['cat_count'] = df_org.groupby('category')['category'].transform('count')
# df_org.drop_duplicates(inplace=True)

# # Clean the text
# stop_words = set(stopwords.words('english'))

# def clean_text(text):
#     text = re.sub('[^a-zA-Z]', ' ', text)
#     text = text.replace(',', '')
#     text = text.lower().split()
#     text = [word for word in text if word not in stop_words]
#     text = ' '.join(text)
#     return text

# df_org['clean_text'] = df_org['text'].apply(clean_text)

# # randomly sample 512 tokens from each row in df['clean_text']
# # some strings are smalle than 512
# df_org['clean_text_sampled'] = df_org['clean_text'].apply(lambda x: ' '.join(random.sample(x.split(' '), 350)) if len(x.split(' ')) >= 350 else x)
# X_bert_org = df_org['clean_text_sampled'].apply(lambda x: pd.Series(bert_encode([str(x)])[0]))

# # setting distance_threshold=0 ensures we compute the full tree.
# model_bert_org = AgglomerativeClustering(distance_threshold=0, n_clusters=None)
# model_bert_org = model_bert_org.fit(np.array(X_bert_org))

Biographical data for each organisation (~130 organisations) is used to create the clusters. From here we show the most common terms used in the organisation name for each cluster.

# cut = 5
# l_matrix = get_linkage_matrix(model_bert_org)
# df_org['cluster'] = fcluster(l_matrix, cut, criterion='maxclust')
# dendrogram(l_matrix, orientation='top', truncate_mode="lastp", p=cut, show_leaf_counts=True)

# all_words = []

# for i in df_org['cluster'].unique():
#     cluster_docs = df_org[df_org['cluster'] == i]
#     # print(i, get_most_common_word(cluster_docs['clean_text']))
#     [all_words.append(i[0]) for idx,i in enumerate(get_most_common_word(cluster_docs['category'], no_of_words=5))]
    
# all_words_to_remove = find_duplicates(all_words, occurences=4)
# all_words_to_remove.extend(['h','co','e','test','Lapham','J.','Jane','','and','of','&','The','SymbioticA','Nevin','Smith'])

# for i in df_org['cluster'].unique():
#     cluster_docs = df_org[df_org['cluster'] == i]
#     # print(i, get_most_common_word(cluster_docs['clean_text']))
#     annot = "\n".join(i[0] for idx,i in enumerate(get_most_common_word(cluster_docs['category'], no_of_words=6,
#                                                                        more_words=all_words_to_remove)) if (idx < 3))
    
#     plt.annotate(annot, xy=(i/df_org['cluster'].nunique()-0.1, 0.05), 
#                  xytext=(i/df_org['cluster'].nunique()-0.1, 0.05), 
#                  xycoords='axes fraction', fontsize=12)
    
#     # annot2 = cluster_docs.sort_values('cat_count', ascending=False)['category'].values[0:3]
#     # annot2 = '\n\n'.join(['\n'.join(wrap(line, 18)) for line in [i.split(',')[0] for i in annot2]])
#     # # annot2 = '\n'.join(wrap(annot2, 18)) # breaks strings into new lines

#     # plt.annotate(annot2, xy=(i/df_org['cluster'].nunique()-0.115, -0.24), 
#     #             xytext=(i/df_org['cluster'].nunique()-0.115, -0.24), 
#     #             xycoords='axes fraction', fontsize=9)

# plt.title("Hierarchical Clustering Dendrogram - BERT, Organisations")

# # make figure bigger
# fig = plt.gcf()
# fig.set_size_inches(14, 10)

# plt.show()

from IPython.display import Image
Image(filename='images/daao_tlc/output5.png')
_images/DAAO_Venues_53_0.png

Temporal analysis of venues

To further explore frequently used terms in place names, we produce an interactive time series of the number of events per decade by associated terms.

Before the visuals, we provide an ordered list of the decades with the most event activity, and also a list of the top 20 most frequent terms used in place names.

events_df = clean_data_v2.copy()

# create a new column with the decade of start year
events_df['decade'] = events_df['start_year'].apply(lambda x: str(x)[:3] + '0')
events_df['decade'] = events_df['decade'].apply(lambda x: '2000' if x == '2020' else x)
events_df['decade'] = events_df['decade'].astype(int)
events_df['decade'].value_counts()

1990    1464
2000    1065
1980    1030
1970     506
2010     207
1960      73
1950      35
1940      13
1900       7
1910       6
1930       5
1920       4
Name: decade, dtype: int64

most_freq_words_dict2

{'Art': 2752,
 'Gallery': 2520,
 'University': 478,
 'Contemporary': 475,
 'Centre': 464,
 'Museum': 451,
 'Experimental': 425,
 'Foundation': 425,
 'City': 286,
 'Modern': 286,
 'National': 235,
 'Institute': 230,
 'Regional': 172,
 'Fine': 133,
 'Museums': 121,
 'Australian': 112,
 'Place': 110,
 'Region': 96,
 'Arts': 72,
 'Corner': 43,
 'Streets': 43,
 'School': 41}

words_by_decade = pd.DataFrame()

for key in most_freq_words_dict2:
    words_this_decade = events_df[events_df['address_prompt'].str.contains(key, na=False)]\
                        .groupby('decade')['address_prompt']\
                        .count()\
                        .reset_index(name='count')\
                        .sort_values(['decade'], ascending=True)
    words_this_decade['term'] = key
    words_by_decade = pd.concat([words_by_decade, words_this_decade], ignore_index=True)

import plotly.express as px
fig = px.line(words_by_decade, x="decade", y="count", color='term',
                title='Count of each term by decade')


# # make figure size bigger
# # fig.update_layout(
# #     autosize=False,
# #     width=700,
# #     height=500,
# # )

fig.show()

# events_df['diff'] = events_df['end_year'] - events_df['start_year']
# # events_df[(events_df['place_name'].str.contains('Contemporary')) & (events_df['diff'] > 1)]

# using start_date, end_date, place_name, calculate the difference between start_date of the first event in an venue and end_date of the last event in that venue
places_with_start_end = []
[places_with_start_end.extend([p, events_df.loc[events_df['address_prompt'] == p, 'start_year'].min(), 
                              events_df.loc[events_df['address_prompt'] == p, 'end_year'].max()] for p in events_df['place_address'].unique())]

places_with_start_end = pd.DataFrame(places_with_start_end)
places_with_start_end.columns = ['address_prompt', 'start_year', 'end_year']
places_with_start_end['diff'] = places_with_start_end['end_year'] - places_with_start_end['start_year']
places_with_start_end = places_with_start_end[places_with_start_end['diff'] >= 0]
# places_with_start_end.sort_values('diff', ascending=False)

Beyond the above time series, we also provide a series of bar charts, however instead of illustrating event activity within a certain decade, we plot the number of venues active across time. We calculate the start and end of a venue’s “lifespan” by using the earliest and latest event date associated with a venue. We do this by filtering on venues with associated terms in the place name.

Most bar charts follow the same upwards trends shown in the above time series, however some terms that highlight some different trajectories consist of “contemporary”, “society”, “experimental” and “space”.

decade_placeholder = pd.DataFrame([0] * 13, index=range(1900, 2020+1, 10)).reset_index().rename(columns={'index':'decade', 0:'count'})
decade_placeholder['decade'] = decade_placeholder['decade'].astype(str)

for term in most_freq_words_dict2:
    # create a wide form of contemporary with each decade as a column and a binary value for whether the venue existed in that decade
    # use start_year as the start and end_year as the end of the venue
    contemporary = places_with_start_end[(places_with_start_end['address_prompt'].str.contains(term))].sort_values('start_year', ascending=True)
    contemporary_wide = pd.DataFrame()

    for i,row in contemporary.iterrows():
        for year in range(int(row['start_year']), int(row['end_year'])+1): 
            contemporary_wide.loc[row['address_prompt'], str(year)[:3] + '0'] = 1

    contemporary_wide = contemporary_wide.\
        fillna(0).\
        reset_index().\
        rename(columns={'index':'address_prompt'}).\
        sum().tail(-1).\
        reset_index().\
        rename(columns={'index':'decade', 0:'count'})
    
    contemporary_wide = pd.merge(contemporary_wide, decade_placeholder, on='decade', how='outer')
    contemporary_wide['count'] = contemporary_wide['count_x'] + contemporary_wide['count_y']
    contemporary_wide = contemporary_wide[['decade', 'count']].sort_values('decade', ascending=True)

    fig = px.bar(contemporary_wide, x="decade", y='count',
                    title=f'Number of venues that existed in each decade, {term}')

    # remove y-axis label
    fig.update_yaxes(title_text='')
    fig.update_xaxes(title_text='')

    # # make figure size bigger
    # fig.update_layout(
    #     autosize=False,
    #     width=700,
    #     height=400,
    # )

    fig.show()

Proportions

We repeat the same temporal visuals as above, however this time we plot the proportions to effectively normalise the data with respect to the number of events across decades, and the number of venues that existed across time, repsectively.

Note that the y-axis limits in the bar charts changes for each term.

words_by_decade_prop = []

# find the proportions of each word in each decade
for i in most_freq_words_dict2.keys():
    for j in events_df['decade'].unique():
        prop = events_df[(events_df['address_prompt'].str.contains(i)) & (events_df['decade'] == j)].shape[0] /events_df[events_df['decade'] == j].shape[0]
        words_by_decade_prop.append([i,j,prop])
    
words_by_decade_prop = pd.DataFrame(words_by_decade_prop)
words_by_decade_prop.columns = ['word','decade','proportion']

# plot proportion of each term by decade
# make it interactive

import plotly.express as px

fig = px.line(words_by_decade_prop.sort_values('decade'), x="decade", y="proportion", color='word',
                title='Proportion of each term by decade')

# make figure size bigger
# fig.update_layout(
#     autosize=False,
#     width=700,
#     height=500,
# )

fig.show()

allplaces_wide = pd.DataFrame()

for i,row in places_with_start_end.iterrows():
    for year in range(int(row['start_year']), int(row['end_year'])+1): 
        allplaces_wide.loc[row['address_prompt'], str(year)[:3] + '0'] = 1

decade_placeholder_forprop = allplaces_wide.\
                            fillna(0).\
                            reset_index().\
                            rename(columns={'index':'address_prompt'}).\
                            sum().tail(-1).\
                            reset_index().\
                            rename(columns={'index':'decade', 0:'count'}).\
                            sort_values('decade', ascending=True)

for term in most_freq_words_dict2:
    # create a wide form of contemporary with each decade as a column and a binary value for whether the venue existed in that decade
    # use start_year as the start and end_year as the end of the venue
    contemporary = places_with_start_end[(places_with_start_end['address_prompt'].str.contains(term))].sort_values('start_year', ascending=True)
    contemporary_wide = pd.DataFrame()

    for i,row in contemporary.iterrows():
        for year in range(int(row['start_year']), int(row['end_year'])+1): 
            contemporary_wide.loc[row['address_prompt'], str(year)[:3] + '0'] = 1

    contemporary_wide = contemporary_wide.\
        fillna(0).\
        reset_index().\
        rename(columns={'index':'address_prompt'}).\
        sum().tail(-1).\
        reset_index().\
        rename(columns={'index':'decade', 0:'count'})
    
    contemporary_wide = pd.merge(contemporary_wide, decade_placeholder_forprop, on='decade', how='outer')
    contemporary_wide['prop'] = np.where(contemporary_wide['count_x'] > 0, 
                                     contemporary_wide['count_x']/contemporary_wide['count_y'], 0)
    contemporary_wide = contemporary_wide[['decade', 'prop']].sort_values('decade', ascending=True)

    fig = px.bar(contemporary_wide, x="decade", y='prop',
                    title=f'Number of venues that existed in each decade, {term}')

    # remove y-axis label
    fig.update_yaxes(title_text='')
    fig.update_xaxes(title_text='')

    # make figure size bigger
    # fig.update_layout(
    #     autosize=False,
    #     width=700,
    #     height=400,
    # )

    fig.show()

previous

AusStage symposium